A generative adversarial network (GAN) is a machine learning method
introduced in [1]}. The basic purpose
of a GAN is to learn how to generate synthetic data based on a training
set of real-world examples. While a traditional use of GANs has been
to generate authentic-looking photographs based on real example images
(for a recent example, see [2]}), they are now
in use across various scientific disciplines. To give brief examples,
variants of GANs have been used
in biology to generate synthetic protein-coding DNA sequences ([3]}),
in physics to simulate subatomic particle collisions at the Large Hadron Collider ([4]}),
in astronomy to de-noise images of galaxies ([5]}),
and in medicine to improve near-infrared fluorescence imaging ([6]}).
GANs have also been used in popular culture and arts
to re-create video games ([7]}),
to make computer-generated art ([8]}),
and to compose music ([9]}).
To put the recent popularity of GANs into perspective,
the original [1]} article
received over 10.000 Google Scholar citations in the year 2020 alone.
For recent surveys of GANs and for further references, see
[11]}, [12]}, or [13]}.
